{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:33:42Z","timestamp":1761597222704},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2010,8,11]],"date-time":"2010-08-11T00:00:00Z","timestamp":1281484800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2011,10]]},"DOI":"10.1007\/s00521-010-0434-0","type":"journal-article","created":{"date-parts":[[2010,8,10]],"date-time":"2010-08-10T06:17:37Z","timestamp":1281421057000},"page":"1117-1128","source":"Crossref","is-referenced-by-count":9,"title":["Rule extraction from artificial neural networks to discover causes of quality defects in fabric production"],"prefix":"10.1007","volume":"20","author":[{"given":"Lale","family":"\u00d6zbak\u0131r","sequence":"first","affiliation":[]},{"given":"Adil","family":"Baykaso\u011flu","sequence":"additional","affiliation":[]},{"given":"Sinem","family":"Kulluk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,8,11]]},"reference":[{"issue":"1","key":"434_CR1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1142\/S0219686704000405","volume":"3","author":"MC Jothishankar","year":"2004","unstructured":"Jothishankar MC, Wu T, Roberts J, Shiau J-Y (2004) Case study: applying data mining to defect diagnosis. J Adv Manuf Syst 3(1):69\u201383","journal-title":"J Adv Manuf Syst"},{"issue":"2","key":"434_CR2","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.ejor.2006.10.015","volume":"183","author":"A Baykaso\u011flu","year":"2007","unstructured":"Baykaso\u011flu A, \u00d6zbak\u0131r L (2007) MEPAR-miner: multi-expression programming for classification rule mining. Eur J Oper Res 183(2):767\u2013784","journal-title":"Eur J Oper Res"},{"issue":"1","key":"434_CR3","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.asoc.2009.08.008","volume":"10","author":"L \u00d6zbak\u0131r","year":"2010","unstructured":"\u00d6zbak\u0131r L, Baykaso\u011flu A, Kulluk S (2010) A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner. Appl Soft Comput 10(1):304\u2013317","journal-title":"Appl Soft Comput"},{"key":"434_CR4","unstructured":"Frawley W, Piatetsky-Shapiro G, Maktheus CW (1992) Knowledge discovery in databases: an overview. AI Magazine 213\u2013238"},{"key":"434_CR5","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2001","unstructured":"Han J, Kamber M (2001) Data mining: concepts and techniques. Academic Press, New York"},{"issue":"2","key":"434_CR6","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1016\/j.eswa.2008.01.057","volume":"36","author":"C Wang","year":"2009","unstructured":"Wang C (2009) Separation of composite defect patterns on wafer bin map using support vector clustering. Expert Syst Appl 36(2):2554\u20132561","journal-title":"Expert Syst Appl"},{"key":"434_CR7","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s00521-007-0139-1","volume":"17","author":"X Hu","year":"2008","unstructured":"Hu X, Zhao Z, Wang S, Wang F, He D, Wu S (2008) Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester. Neural Comput Appl 17:399\u2013403","journal-title":"Neural Comput Appl"},{"key":"434_CR8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.eswa.2006.04.014","volume":"33","author":"C Chien","year":"2007","unstructured":"Chien C, Wang W, Cheng J (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33:192\u2013198","journal-title":"Expert Syst Appl"},{"key":"434_CR9","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1016\/j.ymssp.2006.06.010","volume":"21","author":"W Sun","year":"2007","unstructured":"Sun W, Chen J, Li J (2007) Decision tree and PCA-based fault diagnosis of rotating machinery. Mech Syst Signal Process 21:1300\u20131317","journal-title":"Mech Syst Signal Process"},{"key":"434_CR10","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ijpe.2006.05.015","volume":"107","author":"S Hsu","year":"2007","unstructured":"Hsu S, Chien C (2007) Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. Int J Prod Econ 107:88\u2013103","journal-title":"Int J Prod Econ"},{"key":"434_CR11","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.rcim.2005.01.001","volume":"21","author":"B Tseng","year":"2005","unstructured":"Tseng B, Kwon Y, Yalcin E (2005) Feature-based rule induction in machining operation using rough set theory for quality assurance. Robot Comput Integr Manuf 21:559\u2013567","journal-title":"Robot Comput Integr Manuf"},{"key":"434_CR12","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1016\/j.cemconres.2004.03.028","volume":"34","author":"A Baykaso\u011flu","year":"2004","unstructured":"Baykaso\u011flu A, Dereli T, Tan\u0131\u015f S (2004) Prediction of cement strength using soft computing techniques. Cement Concrete Res 34:2083\u20132090","journal-title":"Cement Concrete Res"},{"issue":"10","key":"434_CR13","doi-asserted-by":"crossref","first-page":"12491","DOI":"10.1016\/j.eswa.2009.04.033","volume":"36","author":"A Baykaso\u011flu","year":"2009","unstructured":"Baykaso\u011flu A, \u00c7evik A, \u00d6zbak\u0131r L, Kulluk S (2009) Generating prediction rules for liquefaction through data mining. Expert Syst Appl 36(10):12491\u201312499","journal-title":"Expert Syst Appl"},{"key":"434_CR14","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1115\/1.2194554","volume":"128","author":"JA Harding","year":"2006","unstructured":"Harding JA, Shahbaz M, Srinivas Kusiak A (2006) Data mining in manufacturing: a review. J Manuf Sci Eng 128:969\u2013976","journal-title":"J Manuf Sci Eng"},{"key":"434_CR15","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/978-3-540-92695-5_14","volume":"5313","author":"L \u00d6zbak\u0131r","year":"2008","unstructured":"\u00d6zbak\u0131r L, Baykaso\u011flu A, Kulluk S (2008) Rule extraction from neural networks via ant colony algorithm for data mining applications. Lect Notes Comput Sci 5313:177\u2013191","journal-title":"Lect Notes Comput Sci"},{"issue":"10","key":"434_CR16","doi-asserted-by":"crossref","first-page":"12295","DOI":"10.1016\/j.eswa.2009.04.058","volume":"36","author":"L \u00d6zbak\u0131r","year":"2009","unstructured":"\u00d6zbak\u0131r L, Baykaso\u011flu A, Kulluk S, Yap\u0131c\u0131 H (2009) TACO-miner: an ant colony based algorithm for rule extraction from trained neural networks. Expert Syst Appl 36(10):12295\u201312305","journal-title":"Expert Syst Appl"},{"issue":"6","key":"434_CR17","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/0950-7051(96)81920-4","volume":"8","author":"R Andrews","year":"1995","unstructured":"Andrews R, Diederich J, Tickle AB (1995) A survey, critique of techniques for extracting rules from trained artificial neural networks. Knowl Based Syst 8(6):373\u2013389","journal-title":"Knowl Based Syst"},{"key":"434_CR18","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.neucom.2005.12.127","volume":"70","author":"ER Hruschka","year":"2006","unstructured":"Hruschka ER, Ebecken NFF (2006) Extracting rules from multilayer perceptrons in classification problems: a clustering-based approach. Neurocomputing 70:384\u2013397","journal-title":"Neurocomputing"},{"key":"434_CR19","doi-asserted-by":"crossref","unstructured":"Santos RT, Nievola JC, Freitas AA (2000) Extracting comprehensible rules from neural network via genetic algorithms. In: Proceedings of 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (ECNN-2000), San Antonio, TX: USA, pp 130\u2013139","DOI":"10.1109\/ECNN.2000.886228"},{"key":"434_CR20","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0377-2217(02)00792-0","volume":"155","author":"R Setiono","year":"2004","unstructured":"Setiono R, Thong JYL (2004) An approach to generate rules from neural networks for regression problems. Eur J Oper Res 155:239\u2013250","journal-title":"Eur J Oper Res"},{"key":"434_CR21","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.asoc.2003.08.004","volume":"4","author":"E Elalfi","year":"2004","unstructured":"Elalfi E, Haque R, Elalami ME (2004) Extracting rules from trained neural network using GA for managing E-business. Appl Soft Comput 4:65\u201377","journal-title":"Appl Soft Comput"},{"key":"434_CR22","doi-asserted-by":"crossref","unstructured":"Markowska-Kaczmar U, Wnuk-Lipinski P (2004) Rule extraction from neural network by genetic algorithm with pareto optimization. In: Artificial Intelligence and Soft Computing- ICAISC 2004, 7th International Conference, Proceedings, Springer, Lecture Notes in Computer Science, vol 3070, pp 450\u2013455","DOI":"10.1007\/978-3-540-24844-6_66"},{"key":"434_CR23","doi-asserted-by":"crossref","unstructured":"Tokinaga S, Lu J, Ikeda Y (2005) Neural network rule extraction by using the genetic programming and its applications to explanatory classifications. IECE Trans Fundamentals E88-A(10):2627\u20132635","DOI":"10.1093\/ietfec\/e88-a.10.2627"},{"key":"434_CR24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s00521-005-0002-1","volume":"15","author":"J Malone","year":"2005","unstructured":"Malone J, McGarry K, Wermter S, Bowerman C (2005) Data mining using rule extraction from Kohonen self-organising maps. Neural Comput Appl 15:9\u201317","journal-title":"Neural Comput Appl"},{"key":"434_CR25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s00521-007-0115-9","volume":"17","author":"JS Heh","year":"2008","unstructured":"Heh JS, Chen JC, Chang M (2008) Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree. Neural Comput Appl 17:297\u2013309","journal-title":"Neural Comput Appl"},{"key":"434_CR26","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.eswa.2007.11.024","volume":"36","author":"H Kahramanl\u0131","year":"2009","unstructured":"Kahramanl\u0131 H, Allahverdi N (2009) Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst Appl 36:1513\u20131522","journal-title":"Expert Syst Appl"},{"key":"434_CR27","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.ejor.2007.09.022","volume":"192","author":"R Setiono","year":"2009","unstructured":"Setiono R, Baesens B, Mues C (2009) A note on knowledge discovery using neural networks and its application to credit card screening. Eur J Oper Res 192:326\u2013332","journal-title":"Eur J Oper Res"},{"key":"434_CR28","unstructured":"Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy. Technical Report N. 91-016, Politecnico di Milano"},{"key":"434_CR29","unstructured":"Hiroyasu T, Miki M, Ono Y, Minami Y (2000) Ant colony for continuous functions. The Science and Engineering, Doshisha University XX (Y)"},{"key":"434_CR30","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.engappai.2004.02.009","volume":"17","author":"N Karabo\u011fa","year":"2004","unstructured":"Karabo\u011fa N, Kalinli A, Karabo\u011fa D (2004) Designing digital IIR filters using ant colony optimisation algorithm. Eng Appl Artif Intell 17:301\u2013309","journal-title":"Eng Appl Artif Intell"},{"key":"434_CR31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10589-005-3907-9","volume":"34","author":"C Tan","year":"2006","unstructured":"Tan C, Yu Q, Ang JH (2006) A dual-objective evolutionary algorithm for rules extraction in data mining. Comput Optim Appl 34:273\u2013294","journal-title":"Comput Optim Appl"},{"issue":"4","key":"434_CR32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/TEVC.2002.802452","volume":"6","author":"RS Parpinelli","year":"2002","unstructured":"Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6(4):321\u2013332","journal-title":"IEEE Trans Evol Comput"},{"key":"434_CR33","unstructured":"Johansson U, L\u00f6fstr\u00f6m T, K\u00f6nig R (2006) Why not use an oracle when you got one? Neural Information Processing\u2014Letters and Reviews 10(8\u20139)"},{"issue":"1","key":"434_CR34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1108\/01445150610645611","volume":"26","author":"J Antony","year":"2006","unstructured":"Antony J, Perry D, Wang C, Kumar M (2006) An application of Taguchi method of experimental design for new product design and development process. Assembly Automat 26(1):18\u201324","journal-title":"Assembly Automat"},{"key":"434_CR35","volume-title":"C4.5: programs for machine learning","author":"R Quinlan","year":"1993","unstructured":"Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Mateo"},{"key":"434_CR36","unstructured":"Frank E, Witten IH (1998) Generating accurate rule sets without global optimization. In: Shavlik J (ed) Machine learning: Proceedings of the 15th International Conference, Morgan Kaufmann Publishers, pp 144\u2013151"},{"key":"434_CR37","doi-asserted-by":"crossref","unstructured":"Kohavi R (1995) The power of decision tables. In: Lavrac N, Wrobel S (eds) Machine learning: Proceedings of the 8th European Conference on Machine Learning (ECML 95), Lecture Notes in Artificial Intelligence, vol 914. Springer, pp 174\u2013189","DOI":"10.1007\/3-540-59286-5_57"},{"key":"434_CR38","unstructured":"John GH, Langley (1995) Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Mateo, pp 338\u2013345"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-010-0434-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-010-0434-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-010-0434-0","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T14:32:53Z","timestamp":1559399573000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-010-0434-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,8,11]]},"references-count":38,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2011,10]]}},"alternative-id":["434"],"URL":"https:\/\/doi.org\/10.1007\/s00521-010-0434-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,8,11]]}}}